{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost Article\n",
    "\n",
    "The data here is taken form the Data Hackathon3.x - http://datahack.analyticsvidhya.com/contest/data-hackathon-3x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/honlan/anaconda/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/Users/honlan/anaconda/lib/python2.7/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn import cross_validation, metrics\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 12, 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data:\n",
    "\n",
    "The data has gone through following pre-processing:\n",
    "1. City variable dropped because of too many categories\n",
    "2. DOB converted to Age | DOB dropped\n",
    "3. EMI_Loan_Submitted_Missing created which is 1 if EMI_Loan_Submitted was missing else 0 | EMI_Loan_Submitted dropped\n",
    "4. EmployerName dropped because of too many categories\n",
    "5. Existing_EMI imputed with 0 (median) - 111 values were missing\n",
    "6. Interest_Rate_Missing created which is 1 if Interest_Rate was missing else 0 | Interest_Rate dropped \n",
    "7. Lead_Creation_Date dropped because made little intuitive impact on outcome\n",
    "8. Loan_Amount_Applied, Loan_Tenure_Applied imputed with missing\n",
    "9. Loan_Amount_Submitted_Missing created which is 1 if Loan_Amount_Submitted was missing else 0 | Loan_Amount_Submitted dropped \n",
    "10. Loan_Tenure_Submitted_Missing created which is 1 if Loan_Tenure_Submitted was missing else 0 | Loan_Tenure_Submitted dropped \n",
    "11. LoggedIn, Salary_Account removed\n",
    "12. Processing_Fee_Missing created which is 1 if Processing_Fee was missing else 0 | Processing_Fee dropped\n",
    "13. Source - top 2 kept as is and all others combined into different category\n",
    "14. Numerical and One-Hot-Coding performed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('train_modified.csv')\n",
    "test = pd.read_csv('test_modified.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((87020, 51), (37717, 50))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target='Disbursed'\n",
    "IDcol = 'ID'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    85747\n",
       "1.0     1273\n",
       "Name: Disbursed, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Disbursed'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a function for modeling and cross-validation\n",
    "\n",
    "This function will do the following:\n",
    "1. fit the model\n",
    "2. determine training accuracy\n",
    "3. determine training AUC\n",
    "4. determine testing AUC\n",
    "5. update n_estimators with cv function of xgboost package\n",
    "6. plot Feature Importance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def modelfit(alg, dtrain, dtest, predictors,useTrainCV=True, cv_folds=5, early_stopping_rounds=50):\n",
    "    \n",
    "    if useTrainCV:\n",
    "        xgb_param = alg.get_xgb_params()\n",
    "        xgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values)\n",
    "        xgtest = xgb.DMatrix(dtest[predictors].values)\n",
    "        cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,\n",
    "            metrics='auc', early_stopping_rounds=early_stopping_rounds)\n",
    "        print cvresult.shape[0]\n",
    "        alg.set_params(n_estimators=cvresult.shape[0])\n",
    "    \n",
    "    #Fit the algorithm on the data\n",
    "    alg.fit(dtrain[predictors], dtrain['Disbursed'],eval_metric='auc')\n",
    "        \n",
    "    #Predict training set:\n",
    "    dtrain_predictions = alg.predict(dtrain[predictors])\n",
    "    dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1]\n",
    "        \n",
    "    #Print model report:\n",
    "    print \"\\nModel Report\"\n",
    "    print \"Accuracy : %.4g\" % metrics.accuracy_score(dtrain['Disbursed'].values, dtrain_predictions)\n",
    "    print \"AUC Score (Train): %f\" % metrics.roc_auc_score(dtrain['Disbursed'], dtrain_predprob)\n",
    "                \n",
    "    feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)\n",
    "    feat_imp.plot(kind='bar', title='Feature Importances')\n",
    "    plt.ylabel('Feature Importance Score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1- Find the number of estimators for a high learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128\n",
      "\n",
      "Model Report\n",
      "Accuracy : 0.9854\n",
      "AUC Score (Train): 0.896723\n"
     ]
    },
    {
     "data": {
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06cFnzczMzMxsfJW5In0MaeTBBQARcQ2w5yAzZWZmZmY27soUpJePiEubpj06iMyYmZmZ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\nDf8LHAYCLgONGl4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10370c450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = [x for x in train.columns if x not in [target, IDcol]]\n",
    "xgb1 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=1000,\n",
    "        max_depth=5,\n",
    "        min_child_weight=1,\n",
    "        gamma=0,\n",
    "        subsample=0.8,\n",
    "        colsample_bytree=0.8,\n",
    "        objective= 'binary:logistic',\n",
    "        nthread=4,\n",
    "        scale_pos_weight=1,\n",
    "        seed=27)\n",
    "modelfit(xgb1, train, test, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
       "       min_child_weight=1, missing=None, n_estimators=128, nthread=4,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'max_depth': [3, 5, 7, 9], 'min_child_weight': [1, 3, 5]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "#Choose all predictors except target & IDcols\n",
    "param_test1 = {\n",
    "    'max_depth':range(3,10,2),\n",
    "    'min_child_weight':range(1,6,2)\n",
    "}\n",
    "gsearch1 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=128, max_depth=5,\n",
    "                                        min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8,\n",
    "                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1, seed=27), \n",
    "                       param_grid = param_test1, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch1.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.83665, std: 0.00842, params: {'max_depth': 3, 'min_child_weight': 1},\n",
       "  mean: 0.83766, std: 0.00886, params: {'max_depth': 3, 'min_child_weight': 3},\n",
       "  mean: 0.83690, std: 0.00834, params: {'max_depth': 3, 'min_child_weight': 5},\n",
       "  mean: 0.84043, std: 0.00778, params: {'max_depth': 5, 'min_child_weight': 1},\n",
       "  mean: 0.84116, std: 0.00598, params: {'max_depth': 5, 'min_child_weight': 3},\n",
       "  mean: 0.84117, std: 0.00590, params: {'max_depth': 5, 'min_child_weight': 5},\n",
       "  mean: 0.83886, std: 0.00550, params: {'max_depth': 7, 'min_child_weight': 1},\n",
       "  mean: 0.83756, std: 0.00594, params: {'max_depth': 7, 'min_child_weight': 3},\n",
       "  mean: 0.83797, std: 0.00343, params: {'max_depth': 7, 'min_child_weight': 5},\n",
       "  mean: 0.82910, std: 0.00595, params: {'max_depth': 9, 'min_child_weight': 1},\n",
       "  mean: 0.83129, std: 0.00527, params: {'max_depth': 9, 'min_child_weight': 3},\n",
       "  mean: 0.83365, std: 0.00635, params: {'max_depth': 9, 'min_child_weight': 5}],\n",
       " {'max_depth': 5, 'min_child_weight': 5},\n",
       " 0.8411668664899846)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch1.grid_scores_, gsearch1.best_params_, gsearch1.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
       "       min_child_weight=2, missing=None, n_estimators=128, nthread=4,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'max_depth': [4, 5, 6], 'min_child_weight': [4, 5, 6]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "#Choose all predictors except target & IDcols\n",
    "param_test2 = {\n",
    "    'max_depth':[4,5,6],\n",
    "    'min_child_weight':[4,5,6]\n",
    "}\n",
    "gsearch2 = GridSearchCV(estimator = XGBClassifier( learning_rate=0.1, n_estimators=128, max_depth=5,\n",
    "                                        min_child_weight=2, gamma=0, subsample=0.8, colsample_bytree=0.8,\n",
    "                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), \n",
    "                       param_grid = param_test2, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch2.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.83988, std: 0.00671, params: {'max_depth': 4, 'min_child_weight': 4},\n",
       "  mean: 0.84046, std: 0.00725, params: {'max_depth': 4, 'min_child_weight': 5},\n",
       "  mean: 0.84083, std: 0.00728, params: {'max_depth': 4, 'min_child_weight': 6},\n",
       "  mean: 0.84054, std: 0.00619, params: {'max_depth': 5, 'min_child_weight': 4},\n",
       "  mean: 0.84117, std: 0.00590, params: {'max_depth': 5, 'min_child_weight': 5},\n",
       "  mean: 0.83986, std: 0.00624, params: {'max_depth': 5, 'min_child_weight': 6},\n",
       "  mean: 0.83934, std: 0.00622, params: {'max_depth': 6, 'min_child_weight': 4},\n",
       "  mean: 0.83950, std: 0.00678, params: {'max_depth': 6, 'min_child_weight': 5},\n",
       "  mean: 0.83899, std: 0.00651, params: {'max_depth': 6, 'min_child_weight': 6}],\n",
       " {'max_depth': 5, 'min_child_weight': 5},\n",
       " 0.8411668664899846)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch2.grid_scores_, gsearch2.best_params_, gsearch2.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
       "       min_child_weight=2, missing=None, n_estimators=128, nthread=4,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'min_child_weight': [6, 8, 10, 12]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "#Choose all predictors except target & IDcols\n",
    "param_test2b = {\n",
    "    'min_child_weight':[6,8,10,12]\n",
    "}\n",
    "gsearch2b = GridSearchCV(estimator = XGBClassifier( learning_rate=0.1, n_estimators=128, max_depth=5,\n",
    "                                        min_child_weight=2, gamma=0, subsample=0.8, colsample_bytree=0.8,\n",
    "                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), \n",
    "                       param_grid = param_test2b, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch2b.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.83986, std: 0.00624, params: {'min_child_weight': 6},\n",
       "  mean: 0.83891, std: 0.00657, params: {'min_child_weight': 8},\n",
       "  mean: 0.83963, std: 0.00668, params: {'min_child_weight': 10},\n",
       "  mean: 0.83928, std: 0.00638, params: {'min_child_weight': 12}],\n",
       " {'min_child_weight': 6},\n",
       " 0.8398577194301471)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch2b.grid_scores_, gsearch2b.best_params_, gsearch2b.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
       "       min_child_weight=5, missing=None, n_estimators=128, nthread=4,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'gamma': [0.0, 0.1, 0.2, 0.3, 0.4]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "#Choose all predictors except target & IDcols\n",
    "param_test3 = {\n",
    "    'gamma':[i/10.0 for i in range(0,5)]\n",
    "}\n",
    "gsearch3 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=128, max_depth=5,\n",
    "                                        min_child_weight=5, gamma=0, subsample=0.8, colsample_bytree=0.8,\n",
    "                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), \n",
    "                       param_grid = param_test3, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch3.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.84117, std: 0.00590, params: {'gamma': 0.0},\n",
       "  mean: 0.83988, std: 0.00674, params: {'gamma': 0.1},\n",
       "  mean: 0.83886, std: 0.00598, params: {'gamma': 0.2},\n",
       "  mean: 0.84009, std: 0.00557, params: {'gamma': 0.3},\n",
       "  mean: 0.83971, std: 0.00746, params: {'gamma': 0.4}],\n",
       " {'gamma': 0.0},\n",
       " 0.8411668664899846)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch3.grid_scores_, gsearch3.best_params_, gsearch3.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117\n",
      "\n",
      "Model Report\n",
      "Accuracy : 0.9854\n",
      "AUC Score (Train): 0.887086\n"
     ]
    },
    {
     "data": {
      "image/png": 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BSRMkzQDmkSrjNwDrRMT8HG8esHZ++XrAfXWrP5CXmZmZmZkNlFajdtwn6dPA/cBrgEvg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\n30vEWAY4Ndq06+xTLlXEOA74TUScmx/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snnWjFzGJdrEYkt5CmqC8KSm9o71d\nPLFj0LGsmAY+XiXtAlwGrEaaEzMPONz2Wb3EMhIj0m2jLmsPMOoC40deOvNrap8fNhnbb6k6BgDb\nb6w6BmDhzL/SLJJeBewNvIxU3/c4YK8KQ2o02ycBJ7WuTrU/pgbWTHZakfF+SY91zk6XdEqXn8v1\nc7sqxo9sK71EdwMVYYK6tUcjU94w2sVi2T4BOGGKdnH1isIa+Hi1fXa+eT8p97uv0qAj0ZEGvgH8\niAFGXTJPcXuy+40gaVXSmVbrkuxlwMG2/1ByHCuSzmjXZPxiOQeWFUPuJI2aN5FyAN8eE2sKtQ/w\n3x3bvgO8oIJYijBuorSkxej+vTyv+HBCDdujUSpvGO3icEzWLn4PqOIq1UDHq6RVSOkq19l+NKev\nHEK6IvnsXgIZiY50a9QFmK+0CuEzSe9taUlL276zy11tIukB0kjLU/Nt8v0lp35arZ1AOtHYM9/f\nL2/bseQ4vg/8AvgpY4vllEojWGPU9nxJawAvBi7Mky8Wq2sJrbqTtD6p07lMx/yCeTSwDVBaUObD\nTGzPHiXVLp5Rt3VWJf3c9jZ9BToL1bA9OohUkeUR4DRSecNPlBxDIaJdLJakdUkn1Mt0zHeqsl3s\n+3iVdEh+7q3AUyT9D/AZ0uJ9PQ+WjFqO9LtIK/n9hbGSLLP60qOka21vOtO2KuIom0awxqjS4hoH\nAsvbXiefmX/N9g4Vh9ZIkl4LvA7YFWjPk3sQON12I0foJH3a9qFDfo1aVANqilFsj+oi2sViSXo9\nqWTtq4Eftj30IHCa7csqCaxPkm4EtrP9t5ya8r/Ai9rKJ/a2vxHrSN8KbGX73qpjqQtJF5FGoE/L\nm+YDbym7QZH0aeDiKmd+S1q9h6sTjSDpWuCFwBWjMkGoDiRtY/vnVccxKEnr275ZaYGZCWxfXeBr\nxUTEHtSlPZpqRLyliVfqol0cDknb2f5pxTEMfLxOUkziV7Y36TemkUjtaPN7mr2M7zAcQMqR/iLp\n4LucNIO5bO8APijpYdJl5dZEpZ5LzQzgyVwuSWfY3r3E1x6WR3J+F/Bk7uvonB2XrG0yzb6S5nc+\n3sDKLu8ljcx9fpLHTJr1HqpRl/bocxW97jBFu1ggSe+z/Xlg946UNwBsT7q40JAUcbyu2lE1Z+X2\n+72286PWkb4NuETSOYxf2bBuK0SVJuc31mFEoQ5lh9qn965dWRTFulRSKwd2R1L1mh9UHFOT3ZT/\n/WWlURSkNZnX9stLeLlGVjaqUC3aoxFNIYl2sVi/zf/eUGkUFHa8vr/j/kAVdEYttWPSlaLqVvi+\nTLlk10FMrJZReuda0j7A2rY/lauJPLPfnKQ+X3/k6uEqLbf+VmAn0hfzecBxHqUPdhhYnoS9MxPb\ngcIGGSRtZLvyL9qmqEt7JOlbtveSdD3jR20bW94w2sXRVebxqi5Xch2pjnSLpKUBbP+/qmOpmqRf\nAccD1zM2AbP0UQhJRwGLk4riP0/S8sB57nEpzgFjeBx4iFyVBXi49RDpAzivrFiKJGklANt3Vx3L\nqJC0BWlW9xqM73g2rlMBIOmHpBVNO9uBrgcZJD3IxMvj95NG799n+7YCQp016tIeSVrZ9l25ysUE\n3VZtqZtoF4uX51ocysR2sbSTwDKP125PcEcqtUPSRsAp5CUeJd0DvMn2rysNrFr/tD3tClol2db2\n5pKuAcizZZcoMwDbc8t8vWFSSv77KPAu0pLrrS/mBbY/XmVsI+JU0uW/cR3PBlu1gJOALwF/IJXT\nFKmm7DrA1cDXSYtfhC7VpT2yfVf+93eSnkWapGfgKpe48mwRol0cutNIHenK2sU6Hq9zqnjRIToG\neK/tNWyvAbwPOLbimKp2pKSPStpG0uatnwriWJQvtxlA0gqMRgelKu8hLbKzpe3l86TNrYAXSXpP\ntaGNhLttn2X7dtu/a/1UHdQAfiRppwH3savto20/aPsB28cA/2L7m6RlyEODSfpX4EpSmbM9gF9I\nOqDaqHoW7eJw3WP7u7Zvsf3b1k8VgdTpeB2p1I7JSpgMWtak6XLZuTeSJgu019Yudba+pDcBrwe2\nII1e7UVa0/70MuMYFXlkf0fb93RsXwk431HPdyCSdiCViryI8ROXv1tZUAPIdWD/L2nwZBF9pA9I\n+jmp+s938qY9SAMXW9ehTnwYjKTfkK4c3pvvrwBcbnu9aiPrXrSLw5VPxndjYrt41pRPGl4sQz9e\nu62NP1KpHcBtkv6DlN4BaRW/2Z63tydpgt+jVby4pMVsP2b7ZEkLgVeQvsT3jIlJA1m888sCUj6g\npMWrCGjEvAVYn5TX/+QJKNDIjjTwBWAb4PoBJly9ATgS+B/S/8UvgP2UVo17VyFRhirdS1pgo+XB\nvK1Jol0crjcAGwNPZ3y7WHpHmgKOV0l72v72NNuO7GY/o9aRPgA4nLEvu8vyttnsBmBZ4K8Vvf6V\n5FqpOVd9NuerF2m6E6NKTppGzJZNGonrwu+BGwapWpAnE75miocrXaQh9E9SqwbwrcAVkr5P6hy9\nFriussD6E+3icG1ddbtY8PF6KPDtqbbZPrGbnYxUR9r234GmLZgwbMsCN0u6ivGXYsoqfxe1ZYdj\nE0kPTLJdwJJlBzOCLpe0ge0bqw6kIK0a+z+izxr7+fL425hYQm+2D1Y03dPzv79lrF4wwPcriGVQ\n0S4O1xWS1rP9mwpjGPh4lfQq0nLnq3QszDIPeKzXgEaiIy1p2ssKVdRMrpFJa2uXaKW2M8gJiqxj\nO5vUZcb/CNsauFbS7aSOZ2Nr6ma3558l8k8/vk+6ynch8HhBcYWKdZZAbHL52GgXh24z4DpJtzK+\nXSytgEFBx+ufSGU7d2X8YiwPkias9mQkJhtKupt06fI04Ao6RkHLrplcZ5K2A+bbfmdJr3cX8FWm\nGJnupY5tCGUZtZq6LZLmkb74Hpzxlyc+NyYUjrDO8rFAlI8N40haZ7LtVVTuKOJ4lbS47UX59nLA\narZ7TmcalY70XGBH0iz7jYFzgNOiAUgkbQbsS5p4eDtwhu2jSnrtkVhBMMw+uUzkdqT8u5/Zvrri\nkPqWF5g5gbHLovcDB7iHlUUlHUGaFf/DIYQYKibpcuAw2xfn+y8DPmV720oDC7UiaWPGt4uV5NEX\ncbxKuoQ0Kr0YaWT6r6Q2rqdR6ZGoI237cdvn2t6fdEn2VlI+4KydSS5p3Vw/+mZgAXAn6cTp5WV1\noluhdPVL6WwwhFqQ9J/AScAKwIrACZI+Um1UA/k68O+217S9JvBOUse6FwcDZ0v6h6QHJD04RT5q\naKantTolALYvAZ5WXTihbiQdRrryvwqwKvANSYdWFE4Rx+syth8glfQ72fZWwA69BjISI9IAkp4C\n7EwalV6TVI7l67b/WGVcVZH0BCmf8a22b83bbrO9dslxLG/7b138Xoxch9rINUo3sf3PfP+pwLVV\nz1jv12T1UOMzF9pJOpO0SmV7+dgX2H59dVGFOsnt4ma2H873lwKuqaJdLOJ4lXQ9sBNp0OQw21dJ\nuq7XuTCjMtnwZGAj4IekRT6iPnE6w9oHuFjSucDpVFBBo5tOdBbVPUKd/Ik0y/+f+f5TgMadlLet\nYnqppKNJo0kG9gYu6XIf69u+eaoVUZuc8hLGifKxYSZ3Mb7fuFjeVoUijtePA+eRUlSukrQ28FM2\nQwAAErBJREFUcEuvgYzEiHQefX0o321/Qz2v3jVqJD2NVF9xPrA9cDJwpu3zKw2sQ4yOhTqQtIDU\nhqwObAlckO/vCFxpe7cKw+uZpIunebirFU4lHWP7wCn2VfoqqSGEckn6IqkdXJPULp6X7+8EXGV7\nj+qiq95IdKRDd3Ie8p7A3rZ3aG3L9bcrFR3pUAeS9p/ucdsnlRVLCGWI8rFhJpLeOt3jto8vMZbC\njldJ65Kqij3T9kZ5IuWuto/oKaboSM9udenAdrumfQihd3ny5AS2P97DPvYEzrX9YJ54uTnwCdvX\nFBRmqECUjw1NUuTxKulS4P3A0a3+h6QbbG/US0wjkSMdBlJabnIuU/hMxq+Kdme+2fNM2RCGJS/E\nMmGUoezJugV6qO32ksAuwE097uM/bH8716J/BfBZ4GvAVsWEGCryLMbKx+5LlI8NU5B0C5O3i+uW\nGEaRx+tStq+UxnWDZufKhmEgpVySkHQQaZXFvwBPtL32xtDTpMQQyrBF2+0lSSlRy0/xu7Vn+/Pt\n9yV9jpTn2IvWaoY7A8fYPifXlg4NZvtx4Fzg3Fz9aj6pfOzhJZdKDfW3XdvtVru4TJkBFHy83pMX\nmTGApD3oY/JkpHbMcmWlduQlRbeyfe+wXyuEYZC00PYLqo6jCHm+xFW2n9PDc84mVS7ZkZTW8Q/S\nBMxNhhNlKEuUjw39kvRL21vM/JuFvmYhx2uu0nEMsC3wd9KCdW/odQXbGJEOZaV2/J60mloItddR\n6m0OaYS6se1lrpfaGjWZC6xEKv3Ui72AVwKfs32fpJVJ+YWhwaJ8bOhWnozX0moXn1JyDIUcr5Lm\nAFvYfkWubjbH9oN97StGpEffdLnJ3S6YUkAMxwPrkfKZHmmL4wvDfu0QetVR6u0x4A5SB/I31UQ0\nGElrtN19DPiL7Z5yAfMl0D/YfiQvx7sxaTWw+4qLNJQtyseGbkm6rO1uq138rO0bS4yhsOO1qNH0\n6EiPuKlyk3tduaeAOD462Xbbh5cZRwizSV55bJHtRfn+esCrgTtsn9njvq4ljUCtSRoN+j6woe1X\nFxp0CCGUQNJ/AfcA36RtQnavg4vRkR5xkZscQvckvQa4rpUjl8vG7Q78DjjY9u1VxtcrST8B3mr7\nFknPAa4ETgU2IOVIf6iHfV1te3NJHwD+YXtBlK0MYfRJejVwQ9uV7A8z1i6+p9ec4rrI1Zk6udfq\nTI3N+Qtdq0VusqSVgA8AG5Jm+wIQq6KFmvkksDWApF2A/UgTWjYjlXr7l+pC68tytltL3u5PKhN1\nkKQlgIVA1x1pYJGk+cCbgNfkbYsXF2oIoaY+TZqQh6SdSUtxv4HULh5NmjvROLbXKmI/c4rYSai1\n20ilYQ6V9N7WTwVxnArcDKwFHE7KrbqqgjhCmI5tP5xv7wYcb3uh7eNIE/Sapv2S4/akJc+x/Shj\nqV7deguwDfBJ27dLWgs4pZAoQwh1Ztut1IfdgONsX2H7a6T5V40kaSlJH5F0TL7/3DyA0pPoSI++\nO0lfnksAT2/7KdsKeRnRRbYvtX0A6Ys9hDqRpKXzjO4dgIvaHltyiufU2XWSPifpPcBzgPMBJC3b\n647yhKIPAlfn+7fb/kyRwYYQamlO7nSK1C7+uO2xUqt2FOwE4FHyaDupvGfPtfEjtWPE1Wgy36L8\n71350tCfaPACF2FkfQm4FngAuMn2LwEkbUYfhfpr4G3AwaQJgju1jbZvAHyulx3l/PHPkU7K15K0\nKfBx27sWF24IoYYWANeQ0kRvsX0lgKRNgD9XGdiA1rG9d05Zw/bD6ljmsBsx2XDE1SU3OV8uuQxY\njfShnEeqAXlWmXGEMBNJqwDPAH5l+4m8bWVg8bbJNhuO0hLKks6wvfsMv7OQdBXpktYEQ0k32N6o\njBhDCNWRtDopjePqvLpgq61c3PYd+f76tm+uLsreSLqcNML+szyReh3SPJIX9rKfGJEefaeSSrvs\nAryDNOHo7rKDsH12vnk/8PKyXz+EbuXVsf7Ysa1zNPoU0up+o6KbWeqLbN/fMWDTa551CKGB8iDC\nnR3bOlcS/AbNahc/RlpufDVJpwIvIs0F6UnkSI++WuQmS1pV0pmS7pb0V0lnSFq17DhCKEhZK4KW\npZtLk7+WtC8wN0/KWQBcPuS4QgjN0ah20fb5pMmTbwZOI610ePG0T5pEdKRH37jc5JzrWUVu8gnA\nWcDKwLOBH+RtITTRbMyJO4iUIvYIaeTpfuCQSiMKIdRJo9pFSRfZvtf2ObbPtn2PpItmfuZ4kdox\n+o6QtAzwPsZyk99TQRwr2W7vOJ8oKb6EQ6iHGUeS8kTFw/JPCCE0kqQlgaWAFSUtx1j7Nw9Ypdf9\nRUd6xNUoN/leSfuRLp9AWuQiVlsMTfVo1QEU7IMz/YKkC4A9bd+X7y8HnG67aYvUhBCG4/GqA+jS\n20lX055NWpiq1ZF+ADiq151F1Y4Rl/OQFwDbkS67XEZa6vgPJcexRo5jmxzH5cBBtn9fZhwhdCNf\n8tthpm1NIelFpIk1a5AGUESPS+FOthx4LBEewuwiaR9S2bhPSloNeIbthVXH1Q9JB9leMOh+YkR6\n9J1AymfcM9/fL2/bscwgbP8OGFdvNqd2fKnMOEKYTtGX/GrkeFJK10L6HzV6QtLqbSUA16BhOZEh\nhP5JOgpYHHgJ8EngIeBrwJZVxtUv2wskbUuqs79Y2/aTe9lPdKRHX51zk99LdKRDvRR6ya9G7rf9\nowH3cRjwU0mXkv5fXgwcOHBkIYSm2DbXW74GwPbfJC1RdVD9knQKsA5pEa7WAIOB6EiHceqcm9yo\nUjlh9Nk+EjiyqEt+NXKxpM8C3yVV3QDA9tXd7sD2uZI2B7bOmw6xfU+xYYYQamyRpDnkK1GSVqDZ\nteS3ADbwgDnO0ZEefQeQcpO/yFhu8purDKhNXBYOtVTUJb8a2Sr/u0XbNtN7TfltSZd1W86e6hdD\nCCPnK8AZwEqSDgf2Ag6vNqSB3AA8C+hccKsnMdlwFpJ0iO1SUiokPcjkHWYBT7UdJ3Ohdqa65Gf7\n3dVFVS1J/0XKhTw1b5oPXGX7w9VFFUIok6QNgVeQvsMvtH1DxSH1TdLFwKbAlYy/UrfrlE+abD/R\nkZ59JN1pe/Wq4wihriTdRAGX/OpE0s6kBVWWbG2z/fEenn8dsKntJ/L9ucA1tjcuOtYQQr3kz/t1\ntjesOpaiSHrpZNttX9rLfmI0cHaK3OQQplfIJb+6kPQ1UjWSlwPHAXuQRmF6tSzwt3x7mWKiCyHU\nne3HJd0maRXbf6w6niL02mGeSnSkZ6eRGWULYUhWBG6UNNAlvxrZ1vbGkq6zfbikzwO9VvH4NHBN\nvhwqUq70h4oONIRQW0sDN0n6Oan0HQC2d6supN7NkHJq2/N62V90pEfUTLnJJYcTQtN8rOoACvaP\n/O/Dkp5NqtyzcrdPliTgp6SKHa2asR+0/edCowwh1NkRVQdQBNtPL3J/0ZEeUUUfKCHMJkVd8quR\nsyUtC3wWuJp0kn1st0+2bUk/tP184KwhxRhCqDHbF1UdQx3FZMMQQujQcUVnCdJqXg/1esmvjiQ9\nBVjS9v09Pu8k4CjbVw0nshBCnXW0i4sBc4FHRqFdHESMSIcQQof2Kzo5reG1jC1E0jiSFgf+jbEa\n0JdIOtr2oh52sxWwn6Q7SPmRrXzCqNoRwizQ0S7OAXYjlY+b1WJEOoQQuiDpGtubVR1HPyQdRxpV\nPylveiPwuO1/7WEfa0y23fbvBo8whNBETW4XixIj0iGE0EFS+yz0OaQVAf9ZUThF2NL2Jm33fyzp\nV908UdKSwDuA5wDXA8fbfmwIMYYQakxSe9WiVrv4aEXh1EZ0pEMIYaLXtN1+DLiDlN7RVI9LWsf2\nbwEkrc3Yio0zOQlYBFwGvArYADh4KFGGEOpsz7bbo9AuFiJSO0IIYcRJ2gE4AbiNlNu8BvAW2xd3\n8dzrc7UOJC0GXGl782HGG0IITREj0iGE0EHSqsAC4EV502XAwbb/UF1U/bN9kaTnAuvlTb+h+0lC\nT05ItP1YmnsZQphtJK0IHACsSVv/0faBVcVUBzEiHUIIHSRdAHwDOCVv2g94g+0dq4uqWJLutL16\nF7/3OGOrmLUWdHqYPlcBCyE0k6SfAb8AFtKWGmb7m5UFVQPRkQ4hhA6SrrW96UzbmkzS722vVnUc\nIYRmGLU2sChzqg4ghBBq6F5J+0mam3/2Iy2rPUpiFCWE0IsfSdqp6iDqJkakQwihQ66ZvADYhtTh\nvBx4t+07Kw2sR5J+wOQdZgHb235aySGFEBpK0t+BZUipXY8ylt61fKWBVSw60iGEMKIkvXS6x21f\nWlYsIYRmkzR3su22uy2lOZKiIx1CCB0krQUcxMTZ6btO9Zwmk3SG7d2rjiOEUG+S9gHWtv2pXN3o\nmbYXVh1XlaIjHUIIHfKqf8eTVvJ7orV9VEdwY5nfEMJMJB0FLA68xPbzJC0PnGd7y4pDq1TUkQ4h\nhIn+afvLVQdRohhRCSHMZFvbm0u6BsD23yQtUXVQVYuOdAghTHSkpI8C5wOPtDbavrq6kEIIoVKL\nJM0hn3hLWoG2K3azVXSkQwhhoucDbwS2Z+yLwvn+KIrlCkMIM/kKcAawkqTDgb2Aw6sNqXqRIx1C\nCB0k3QpsYPvRqmMpg6SdbJ9fdRwhhPqRtJjtx/LtDYFXkE6+L7R9Q6XB1UB0pEMIoYOk7wEH2v5r\n1bEMQtL1TF1H2rY3LjmkEELDSLra9uZVx1FXkdoRQggTLQvcLOkqxudIN6383S5VBxBCaLxI/ZpG\njEiHEEKHqRYyGdXydyGEMBVJfwC+MNXjtqd8bDaIEekQQujQ2WGWtB0wH2hUR1rSg4xP7VC+30rt\nmFdJYCGEJpkLLE2MTE8qOtIhhDAJSZsB+wJ7AreTZqs3zUXAs4DvAqfbvrPieEIIzXOX7Y9XHURd\nRUc6hBAySeuSRp7nA/cA3ySlwL280sD6ZPt1kpYBdgOOlbQk6T2dbvtv1UYXQmiIrkaiJS1n++/D\nDqZuIkc6hBAySU8AlwFvtX1r3nab7bWrjWxweSGFfYAvA5+a7XmNIYTuSFq+mxPv2VrdI0akQwhh\nzG6kzubFks4FTqfheYGStiWNsL8Y+CnwetuXVRtVCKEperh61ei2sl8xIh1CCB0kPQ14LakDuj1w\nMnBm0xYtkXQHcB/phODHwGPtj8eS5yGEoszWEenoSIcQwjQkLUeacLi37R1a25qQCyjpEiZfkAVS\n1Y5RXfI8hFCy6EiHEELoymz9wgghhKlIusb2ZlXHUbY5VQcQQggN1IhcQEkfaLu9Z8djnyo/ohBC\nk0maK+nZklZv/bQ9vENlgVUoOtIhhNC7plzK26ft9qEdj72yzEBCCM0m6SDgL8AFwDn55+zW47O1\npGZU7QghhNGlKW5Pdj+EEKZzMLCe7XurDqROYkQ6hBB615ROqKe4Pdn9EEKYzu+B+6sOom5ismEI\nIUxC0lzgmbRduWstsd3tAgVVk/Q48BCp4/9U4OHWQ8CSthevKrYQQrNIOh5Yj5TS8Uhr+2xf3ClS\nO0IIoUPOBfwoKR/wibzZwMbQnFxA23OrjiGEMDLuzD9L5J9AjEiHEMIEkm4FtopcwBBCCNOJEekQ\nQpgocgFDCKGNpJWADwAbAku2ts/2hZ2iIx1CCBPdBlwiKXIBQwghORX4JrAL8A5gf+DuSiOqgehI\nhxDCRJELGEII461g+3hJB9u+FLhU0lVVB1W16EiHEEIH24dXHUMIIdTMovzvXZJ2Bv4ELF9hPLUQ\nHekQQugQuYAhhDDBEZKWAd4HLADmAe+pNqTqRdWOEELoIOl8Ui7g/6EtF9D2BysNLIQQQq3EyoYh\nhDDRCraPBxbZvtT2AUCMRocQZi1Jq0o6U9Ldkv4q6QxJq1YdV9WiIx1CCBONywWUtBmRCxhCmN1O\nAM4CVgaeDfwgb5vVIrUjhBA6SNoFuAxYjbFcwMNtn1VpYCGEUBFJ19redKZts01MNgwhhA62z843\n7wdeXmUsIYRQE/dK2g84Ld+fD8z61V8jtSOEEDpELmAIIUxwALAX8GfgLmAP4M1VBlQH0ZEOIYSJ\nIhcwhBDa2P6d7V1tr2T7GbZfB+xedVxVixzpEELoELmAIYQwM0l32l696jiqFCPSIYQw0b2S9pM0\nN//sR+QChhBCJ1UdQNWiIx1CCBNFLmAIIcxs1qc1RGpHCCF0QdIhtr9UdRwhhFAmSQ8yeYdZwFNt\nz+oKcNGRDiGELkQuYAghhE6R2hFCCN2Z9bmAIYQQxouOdAghdCcu34UQQhhnVue1hBBCu5lyAUsO\nJ4QQQs1FjnQIIYQQQgh9iNSOEEIIIYQQ+hAd6RBCCCGEEPoQHekQQgghhBD6EB3pEEIIIYQQ+vD/\nAa/rODYkJ6NkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11699efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = [x for x in train.columns if x not in [target, IDcol]]\n",
    "xgb2 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=1000,\n",
    "        max_depth=5,\n",
    "        min_child_weight=5,\n",
    "        gamma=0,\n",
    "        subsample=0.8,\n",
    "        colsample_bytree=0.8,\n",
    "        objective= 'binary:logistic',\n",
    "        nthread=4,\n",
    "        scale_pos_weight=1,\n",
    "        seed=27)\n",
    "modelfit(xgb2, train, test, predictors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tune subsample and colsample_bytree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
       "       min_child_weight=5, missing=None, n_estimators=117, nthread=4,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=27, silent=True, subsample=0.8),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'subsample': [0.6, 0.7, 0.8, 0.9], 'colsample_bytree': [0.6, 0.7, 0.8, 0.9]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "#Choose all predictors except target & IDcols\n",
    "param_test4 = {\n",
    "    'subsample':[i/10.0 for i in range(6,10)],\n",
    "    'colsample_bytree':[i/10.0 for i in range(6,10)]\n",
    "}\n",
    "gsearch4 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=117, max_depth=5,\n",
    "                                        min_child_weight=5, gamma=0, subsample=0.8, colsample_bytree=0.8,\n",
    "                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), \n",
    "                       param_grid = param_test4, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch4.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.83903, std: 0.00677, params: {'subsample': 0.6, 'colsample_bytree': 0.6},\n",
       "  mean: 0.83701, std: 0.00672, params: {'subsample': 0.7, 'colsample_bytree': 0.6},\n",
       "  mean: 0.83869, std: 0.00740, params: {'subsample': 0.8, 'colsample_bytree': 0.6},\n",
       "  mean: 0.83881, std: 0.00769, params: {'subsample': 0.9, 'colsample_bytree': 0.6},\n",
       "  mean: 0.83743, std: 0.00659, params: {'subsample': 0.6, 'colsample_bytree': 0.7},\n",
       "  mean: 0.83947, std: 0.00729, params: {'subsample': 0.7, 'colsample_bytree': 0.7},\n",
       "  mean: 0.84047, std: 0.00568, params: {'subsample': 0.8, 'colsample_bytree': 0.7},\n",
       "  mean: 0.83973, std: 0.00881, params: {'subsample': 0.9, 'colsample_bytree': 0.7},\n",
       "  mean: 0.83945, std: 0.00708, params: {'subsample': 0.6, 'colsample_bytree': 0.8},\n",
       "  mean: 0.83942, std: 0.00635, params: {'subsample': 0.7, 'colsample_bytree': 0.8},\n",
       "  mean: 0.84088, std: 0.00592, params: {'subsample': 0.8, 'colsample_bytree': 0.8},\n",
       "  mean: 0.84073, std: 0.00632, params: {'subsample': 0.9, 'colsample_bytree': 0.8},\n",
       "  mean: 0.83960, std: 0.00654, params: {'subsample': 0.6, 'colsample_bytree': 0.9},\n",
       "  mean: 0.84043, std: 0.00652, params: {'subsample': 0.7, 'colsample_bytree': 0.9},\n",
       "  mean: 0.84175, std: 0.00630, params: {'subsample': 0.8, 'colsample_bytree': 0.9},\n",
       "  mean: 0.84159, std: 0.00704, params: {'subsample': 0.9, 'colsample_bytree': 0.9}],\n",
       " {'colsample_bytree': 0.9, 'subsample': 0.8},\n",
       " 0.8417494453556909)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch4.grid_scores_, gsearch4.best_params_, gsearch4.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tune subsample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
       "       min_child_weight=5, missing=None, n_estimators=117, nthread=4,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=27, silent=True, subsample=0.9),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'subsample': [0.8, 0.85, 0.9, 0.95], 'colsample_bytree': [0.7, 0.75, 0.8, 0.85]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "#Choose all predictors except target & IDcols\n",
    "param_test5 = {\n",
    "    'subsample':[i/100.0 for i in range(80,100,5)],\n",
    "    'colsample_bytree':[i/100.0 for i in range(70,90,5)]\n",
    "}\n",
    "gsearch5 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=117, max_depth=5,\n",
    "                                        min_child_weight=5, gamma=0, subsample=0.9, colsample_bytree=0.8,\n",
    "                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), \n",
    "                       param_grid = param_test5, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch5.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.84047, std: 0.00568, params: {'subsample': 0.8, 'colsample_bytree': 0.7},\n",
       "  mean: 0.84032, std: 0.00607, params: {'subsample': 0.85, 'colsample_bytree': 0.7},\n",
       "  mean: 0.83973, std: 0.00881, params: {'subsample': 0.9, 'colsample_bytree': 0.7},\n",
       "  mean: 0.83922, std: 0.00851, params: {'subsample': 0.95, 'colsample_bytree': 0.7},\n",
       "  mean: 0.83916, std: 0.00558, params: {'subsample': 0.8, 'colsample_bytree': 0.75},\n",
       "  mean: 0.83969, std: 0.00538, params: {'subsample': 0.85, 'colsample_bytree': 0.75},\n",
       "  mean: 0.83918, std: 0.00669, params: {'subsample': 0.9, 'colsample_bytree': 0.75},\n",
       "  mean: 0.83939, std: 0.00846, params: {'subsample': 0.95, 'colsample_bytree': 0.75},\n",
       "  mean: 0.84088, std: 0.00592, params: {'subsample': 0.8, 'colsample_bytree': 0.8},\n",
       "  mean: 0.84081, std: 0.00738, params: {'subsample': 0.85, 'colsample_bytree': 0.8},\n",
       "  mean: 0.84073, std: 0.00632, params: {'subsample': 0.9, 'colsample_bytree': 0.8},\n",
       "  mean: 0.83992, std: 0.00788, params: {'subsample': 0.95, 'colsample_bytree': 0.8},\n",
       "  mean: 0.84072, std: 0.00720, params: {'subsample': 0.8, 'colsample_bytree': 0.85},\n",
       "  mean: 0.84028, std: 0.00627, params: {'subsample': 0.85, 'colsample_bytree': 0.85},\n",
       "  mean: 0.84108, std: 0.00647, params: {'subsample': 0.9, 'colsample_bytree': 0.85},\n",
       "  mean: 0.84025, std: 0.00821, params: {'subsample': 0.95, 'colsample_bytree': 0.85}],\n",
       " {'colsample_bytree': 0.85, 'subsample': 0.9},\n",
       " 0.8410805721761925)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch5.grid_scores_, gsearch5.best_params_, gsearch5.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Got the same value as assument and no change requried."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try regularization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
       "       min_child_weight=5, missing=None, n_estimators=117, nthread=4,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=27, silent=True, subsample=0.9),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'reg_alpha': [1e-05, 0.01, 0.1, 1, 100]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "#Choose all predictors except target & IDcols\n",
    "param_test6 = {\n",
    "    'reg_alpha':[1e-5, 1e-2, 0.1, 1, 100]\n",
    "}\n",
    "gsearch6 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=117, max_depth=5,\n",
    "                                        min_child_weight=5, gamma=0, subsample=0.9, colsample_bytree=0.8,\n",
    "                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), \n",
    "                       param_grid = param_test6, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch6.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.84073, std: 0.00632, params: {'reg_alpha': 1e-05},\n",
       "  mean: 0.84028, std: 0.00779, params: {'reg_alpha': 0.01},\n",
       "  mean: 0.84133, std: 0.00657, params: {'reg_alpha': 0.1},\n",
       "  mean: 0.84087, std: 0.00687, params: {'reg_alpha': 1},\n",
       "  mean: 0.81346, std: 0.01464, params: {'reg_alpha': 100}],\n",
       " {'reg_alpha': 0.1},\n",
       " 0.841328703026386)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch6.grid_scores_, gsearch6.best_params_, gsearch6.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.8,\n",
       "       gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
       "       min_child_weight=5, missing=None, n_estimators=117, nthread=4,\n",
       "       objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=27, silent=True, subsample=0.9),\n",
       "       fit_params={}, iid=False, n_jobs=4,\n",
       "       param_grid={'reg_alpha': [0, 0.001, 0.005, 0.01, 0.05]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grid seach on subsample and max_features\n",
    "#Choose all predictors except target & IDcols\n",
    "param_test7 = {\n",
    "    'reg_alpha':[0, 0.001, 0.005, 0.01, 0.05]\n",
    "}\n",
    "gsearch7 = GridSearchCV(estimator = XGBClassifier( learning_rate =0.1, n_estimators=117, max_depth=5,\n",
    "                                        min_child_weight=5, gamma=0, subsample=0.9, colsample_bytree=0.8,\n",
    "                                        objective= 'binary:logistic', nthread=4, scale_pos_weight=1,seed=27), \n",
    "                       param_grid = param_test7, scoring='roc_auc',n_jobs=4,iid=False, cv=5)\n",
    "gsearch7.fit(train[predictors],train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.84073, std: 0.00632, params: {'reg_alpha': 0},\n",
       "  mean: 0.84053, std: 0.00741, params: {'reg_alpha': 0.001},\n",
       "  mean: 0.83990, std: 0.00747, params: {'reg_alpha': 0.005},\n",
       "  mean: 0.84028, std: 0.00779, params: {'reg_alpha': 0.01},\n",
       "  mean: 0.84071, std: 0.00702, params: {'reg_alpha': 0.05}],\n",
       " {'reg_alpha': 0},\n",
       " 0.8407317315997874)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch7.grid_scores_, gsearch7.best_params_, gsearch7.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "146\n",
      "\n",
      "Model Report\n",
      "Accuracy : 0.9854\n",
      "AUC Score (Train): 0.894136\n"
     ]
    },
    {
     "data": {
      "image/png": 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HHt4Xg6RvSh6e8dLMzJrFTTvMzLKmXFk3M7OpzxVpM1ui1NHMpWpTG2hGM5em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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c39a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb3 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=1000,\n",
    "        max_depth=5,\n",
    "        min_child_weight=5,\n",
    "        gamma=0,\n",
    "        subsample=0.9,\n",
    "        colsample_bytree=0.8,\n",
    "        reg_alpha=0,\n",
    "        objective= 'binary:logistic',\n",
    "        nthread=4,\n",
    "        scale_pos_weight=1,\n",
    "        seed=27)\n",
    "modelfit(xgb3, train, test, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1499\n",
      "\n",
      "Model Report\n",
      "Accuracy : 0.9854\n",
      "AUC Score (Train): 0.897278\n"
     ]
    },
    {
     "data": {
      "image/png": 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EzM/NP2ZHxBaSZgAREUfn9OcBRwB3N9bJy/cBdoqIg5rE9CgiZmZmZtZX7UYRKTVMX02F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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1176c6ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb4 = XGBClassifier(\n",
    "        learning_rate =0.01,\n",
    "        n_estimators=5000,\n",
    "        max_depth=5,\n",
    "        min_child_weight=5,\n",
    "        gamma=0,\n",
    "        subsample=0.9,\n",
    "        colsample_bytree=0.8,\n",
    "        reg_alpha=0,\n",
    "        objective= 'binary:logistic',\n",
    "        nthread=4,\n",
    "        scale_pos_weight=1,\n",
    "        seed=27)\n",
    "modelfit(xgb4, train, test, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
